A validation of eddy covariance technique for measuring crop evapotranspiration on different time scales in the North China Plain
Bibliographic record
Abstract
Accurate determination of evapotranspiration (ET) has tremendous potential in guiding irrigation and improving the efficiency of water resources utilization in the North China Plain. Eddy covariance (EC) method is currently a popular method for determining field-scale ET. However, due to varying foot print and unclosed energy balance, the applicability of EC in different regions needs to be tested and corrected. In present work, we compared the ET of the winter wheat – summer maize rotation cropland measured by the EC method with the ET measured by large-scale lysimeters on different time scales. The degree of energy balance closure of EC measurements in this region is 78%. After adjusted by using Bowen ratio forced closure method, the ET monitored by EC is comparable with those monitored by large-scale lysimeters. The results also indicated that the consistency of the observed ET by the EC and lysimeters got better with an increasing time scale, especially for the multi-year average ET values with a relative deviation of less than 1%. The short-time events such as irrigation and precipitation and the mismatch of the varying footprint area of the EC and the small fixed source area of the lysimeter should be responsible for the discrepancy of ET in two methods on daily scale. However, the factors of crop biomass, total available water, and local climate condition exert more effects on the observed ET on large time scale. Overall, the EC technique is responsible for ET measurement of winter wheat – summer maize rotation cropland of the North China Plain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".